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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Quasim, Mohammad Tabrez Mobarak, Mohammad Mufareh Nisa, Khair Ul Meraj, Mohammad Khan, Mohammad Zunnun College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia College of Applied Computer Science King Saud University Saudi Arabia

ISBN: (纸本)9798350397284

Global healthcare systems are going through substantial changes in access, data processing, monitoring, and healthcare. By 2020, it's anticipated that connected and data-capturing equipment will have produced around 2314 exabytes of healthcare data. Cybercriminals work very hard to get access to medical information. Due to this challenge, the market for cybersecurity is predicted to reach $27.1 billion in 2026. To collect data for clinical research, a central repository will be formed with blockchain technology. This article proposes a secure mechanism utilizing Blockchain to guarantee the security of electronic health records (EHR). Databases, the Internet of Things, sensors, and other computing resources are part of the architecture. In comparison to the conventional healthcare system, the security and privacy of EHR will be improved by this framework for security © 2023 IEEE.

关键词： Blockchain

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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2023 International Conference on Electrical Engineering and Informatics, ICEEI 2023

作者： Jeremy, Jason Hidayat, Egi Muhammad Idris Siregar, Simon Trilaksono, Bambang Riyanto Habibullah, Natsir School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Telkom University Faculty of Applied Science Department Computer Engineering Bandung Indonesia

ISBN: (纸本)9798350381290

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG. © 2023 IEEE.

关键词： Model predictive control

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Efficient Vehicle Detection and Classification using YOLO v8 for Real-Time Applications

Efficient Vehicle Detection and Classification using YOLO v8...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350308167

In the realm of real-time applications such as autonomous driving and surveillance, efficient vehicle detection stands as a paramount concern. The revolutionary You Only Look Once (YOLO) framework, renowned for its rapid object recognition, has reshaped this landscape. This study delves into the fusion of real-time vehicle detection and YOLO v8, optimizing its architecture for swift and precise identification. Empirical validation underscores the model's prowess, achieving a remarkable accuracy of 97.9% on mAP50 and 91.3% on mAP50-95. This achievement not only attests to the model's accuracy but also reaffirms its real-time processing capabilities. The implications resonate across transportation safety and urban planning, promising transformative potential. This research underscores the power of YOLO v8 in intelligent object detection, poised to reshape and enhance various domains. © 2023 IEEE.

关键词： mAP50 mAP50-95 Object Detection Vehicle Detection YOLO Framework

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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A Comparative Study of Secure and Efficient Data Duplication Mechanisms for Cloud-Based IoT Applications 1st

A Comparative Study of Secure and Efficient Data Duplication...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Baligodugula, Vishnu Vardhan Amsaad, Fathi Tadepalli, Varshitha Vunnam Radhika, Vojjala Sanjana, Yetukuri Shiva, Sai Meduri, Supriya Maabreh, Majdi Alsaadi, Naser Darwish, Omar Razaque, Abdul Tashtoush, Yahya Computer Science Department Wright State University DaytonOH United States Department of Information Technology The Hashemite University Zarqa Jordan Computer Science Department Taibah University Medina Saudi Arabia Information Security and Applied Computing Eastern Michigan University YpsilantiMI United States Department of Cybersecurity IITU Almaty Kazakhstan Computer Science Department Jordan University of Science and Technology Irbid22110 Jordan

ISBN: (纸本)9783031337420

Cloud-based processes store enormous amounts of data to support several daily applications, especially in intelligent systems and the Industrial Internet of Things (IIoT) applications. The advancement of hosted cloud storage services endorses the productivity of IIoT applications. It is critical to enabling businesses to increase data security, privacy, efficiency, accessibility, and flexibility. There are many concerns for cloud providers, including storage overhead, upload/download bandwidth, and data security and privacy. Utilizing secure data deduplication technology, cloud service providers may maximize their available storage capacity, resulting in effective and secure disk space use. This review focuses on a comparative study of secure data deduplication mechanisms for cloud-based applications, including MD5 and various versions of SHA. These techniques remove duplicate data in different storage levels and enhance users’ data integrity, confidentiality, and privacy protection. It is computationally more efficient than traditional compression methods, which may support IoT applications to manage the exponential growth of their data. We also present an overview and classification of the most recent research on data deduplication based on essential mechanisms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud storage

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A Platform for Automated Solar Data Analysis Using Machine Learning 9

A Platform for Automated Solar Data Analysis Using Machine L...

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9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

作者： Younis, Ola Tashtoush, Yahya Alomari, Mohammad Darwish, Omar Department of Computer Science University of Liverpool Liverpool United Kingdom Computer Science Department Jordan University of Science and Technology Jordan Applied Science Research Center Applied Science Private University Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti United States

ISBN: (纸本)9798350320480

This paper presents a computer platform for the automated analysis of associations among different solar events and activities. This computer tool enables the advanced learning by implementing many associations' algorithms to analyze years of solar catalogues data and to study the associations among solar flares, eruptive filaments per prominences and Coronal Mass Ejections (CMEs). The aim is to combine all solar data catalogues in one dynamic space weather database that can be easily used in the analysis of solar activities and features. The computer tool identifies patterns of associations and provides numerical representations that can be used as inputs to the machine learning algorithms to provide computerized learning rules that can be developed in the future within the context of a real-time prediction system. © 2022 IEEE.

关键词： Data mining

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Development of a Leg-wheel Mobile Robot using Switchable Permanent Magnetic Wheels for Bridge Inspection

Development of a Leg-wheel Mobile Robot using Switchable Per...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Uahchinkul, Chinvudh Muramatsu, Satoshi Inagaki, Katsuhiko Tokai University Course of Science and Technology Kanagawa259-1292 Japan Tokai University Department of Applied Computer Engineering Kanagawa259-1292 Japan

ISBN: (纸本)9798350325706

In recent years, old bridges have increased and require inspection to detect any damaged parts and maintenance to decrease the risk of falling. Bridge inspection robots have developed rapidly to replace human labor. This paper presents a new development of an inspection robot using switchable permanent magnetic wheels rather than typical permanent magnetic wheels to control the adsorption force of each wheel, which can increase the maximum adsorption force of magnetic wheels compared to the previous design that needed to limit the adsorption force for successfully moving between surfaces. Furthermore, this robot has a total of six wheels that connect with leg structures to change its posture for moving to other surfaces on different paths. The movement process of the robot for changing the surface in each path is presented in this paper. As a result of the robot's performance, it can move and change to another surface in various paths and shows the potential to inspect the complex environment of the steel bridge. © 2023 IEEE.

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Multi-agent Reinforcement Learning Based Approach for Vehicle Routing Problem 1

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2nd International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022

作者： Singh, Jagdeep Dhurandher, Sanjay Kumar Woungang, Isaac Ngatched, Telex Magloire N. Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science Toronto Metropolitan University Toronto Canada Faculty of Engineering and Applied Science Grenfell Campus Memorial University St. John’s Canada

ISBN: (数字)9783031252716

ISBN: (纸本)9783031252709

Multi-Vehicle routing to service consumers in dynamic and unpredictable surroundings such as congested urban areas is a difficult operation that needs robust and flexible planning. Value iteration networks hold promise for planning vehicle routing problems. Conventional approaches aren’t usually constructed for real-life settings, and they are too slow to be useful in real time. In comparison, the Vehicle Routing Problem with Value Iteration Network (VRP-VIN) offers a neural network model based on graphs that can execute multi-agent routing in a highly dispersed but connected graph with constantly fluctuating traffic conditions using learned value iteration. Furthermore, the model’s communication module allows vehicles to work better in a cooperative manner online and can easily adapt to changes. A virtual environment is constructed to simulate real-world mapping by self-driving vehicles with uncertain traffic circumstances and minimal edge coverage. This method beats standard solutions based on overall cost and run time. Experiments show that the model achieves a total cost difference of 3% when compared with a state-of-art solver having global information. Also, after being trained with only 2 agents on networks with 25 nodes, can easily generalize to a scenario having additional agents (or nodes). © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Neural network models

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